Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis

Gonthier, Jérôme F.; Radin, Maxwell D.; Buda, Corneliu; Doskocil, Eric J.; Abuan, Clena M.; Romero, Jhonathan

doi:10.1103/physrevresearch.4.033154

Cited by 64 publications

(40 citation statements)

References 69 publications

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“…As such, they are not guaranteed to yield better approximations with respect to classical methods. Moreover, as the molecule size increases, VQE requires a larger number of sub cycles for Hamiltonian averaging (because of more terms in the Hamiltonian), projecting to unfeasibly long runtime for systems of practical importance [ 90 , 102 , 106 , 107 ]. In addition, the training/optimization of the ansatz parameters (the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol{\theta}$\end{document} in Figure 3 ) has already been proven to be nondetermistic polynomial-time hard (NP-hard) [ 108 ].…”

Section: Quantum Algorithms For the Molecular Modelling Of Biomoleculesmentioning

confidence: 99%

Quantum computing algorithms: getting closer to critical problems in computational biology

Marchetti

Nifosı̀

Martelli

et al. 2022

Briefings in Bioinformatics

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The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

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Section: Quantum Algorithms For the Molecular Modelling Of Biomoleculesmentioning

confidence: 99%

Quantum computing algorithms: getting closer to critical problems in computational biology

Marchetti

Nifosı̀

Martelli

et al. 2022

Briefings in Bioinformatics

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“…However, it is expected that this barrier will naturally be addressed as quantum hardware matures. Another relates to the number of measurements required by VQS algorithms to attain chemical accuracy competitive with existing classical simulation techniques [ 148 , 149 ]. Fortunately, substantial recent work suggests that this measurement problem may not be insurmountable [ 150 – 153 ].…”

Section: The Pursuit Of Nisq Advantagesmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

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Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

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“…The efficacy of these approaches has been demonstrated in ref. 85. Such techniques are in particular important in light of stochastic errors when sampling the RDMs.…”

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of the variational quantum eigensolver for molecular systems by the explicitly-correlated perturbative [2]_R12-correction

Schleich

Kottmann

Aspuru‐Guzik

2022

Phys. Chem. Chem. Phys.

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Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis

Cited by 64 publications

References 69 publications

Quantum computing algorithms: getting closer to critical problems in computational biology

Quantum computing algorithms: getting closer to critical problems in computational biology

Biology and medicine in the landscape of quantum advantages

Improving the accuracy of the variational quantum eigensolver for molecular systems by the explicitly-correlated perturbative [2]_R12-correction

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Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis

Cited by 64 publications

References 69 publications

Quantum computing algorithms: getting closer to critical problems in computational biology

Quantum computing algorithms: getting closer to critical problems in computational biology

Biology and medicine in the landscape of quantum advantages

Improving the accuracy of the variational quantum eigensolver for molecular systems by the explicitly-correlated perturbative [2]R12-correction

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Product

Resources

About

Improving the accuracy of the variational quantum eigensolver for molecular systems by the explicitly-correlated perturbative [2]_R12-correction